A method and device for celestial body imaging based on very long baseline interferometric array data

Abstract

The present specification discloses a method and device for celestial body imaging for very long baseline interferometric array data. The method comprises: calibrating the initial visibility data through a calibration process to obtain calibrated visibility data; determining the data to be imaged for the target celestial body according to the calibrated visibility data, and starting the imaging process; through the imaging process, identifying the data to be imaged associated with the abnormal antenna in the data to be imaged, so as to ensure that the data related to the non-abnormal antenna and the data associated with the abnormal antenna are processed separately under the condition of eliminating the interference of the abnormal antenna, and then imaging the target celestial body. The input parameters of all steps of the calibration process and the imaging process are obtained by calculation from the observation information, and are adapted to the visibility data processing of different arrays. This scheme realizes the automatic processing and automatic imaging of visibility data of different arrays, improves the efficiency and accuracy of data processing in the process of imaging celestial bodies, and ensures the reliability of imaging quality.

Description

Technical Field

[0001] The present invention relates to the field of radio astronomy, and in particular to a method and device for celestial body imaging based on very long baseline interferometer array data. Background Art

[0002] Very Long Baseline Interferometry (VLBI) is a radio astronomy technique used for high-resolution observation of celestial bodies. This technique allows multiple astronomical telescopes (antennas) that are far apart to simultaneously observe the same celestial target, forming a giant telescope (baseline) with an aperture size equivalent to the maximum distance between the telescopes, thereby improving the observation effect. Because this technology has the outstanding ability to provide high-precision positioning and high-resolution images for the observed target, its application scope now widely covers many key scientific fields such as deep space lunar exploration, satellite exploration, black hole detection, and astrophysical research, and plays an irreplaceable and important role in these fields.

[0003] However, when imaging the target celestial body through the observation data collected through the baseline, a large amount of observation data needs to be processed and analyzed. This process currently usually needs to be manually processed and calculated by scientific researchers, resulting in low imaging efficiency and prone to human errors.

[0004] Therefore, how to improve the efficiency and accuracy of processing observation data is an urgent problem to be solved. Summary of the invention

[0005] This specification provides a method and device for celestial body imaging based on very long baseline interferometry array data to partially solve the above-mentioned problems existing in the prior art.

[0006] This manual adopts the following technical solutions:

[0007] This specification provides a method for astronomical imaging based on very long baseline interferometry array data, comprising:

[0008] receiving an imaging request for a target celestial body and starting a calibration process according to the imaging request;

[0009] Obtaining, through the calibration process, visibility data of the target celestial body collected by each baseline in a very long baseline interferometer array as initial visibility data corresponding to each baseline;

[0010] For each baseline, the initial visibility data corresponding to the baseline is calibrated to obtain calibrated visibility data corresponding to the baseline;

[0011] Determine the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, and start the imaging process;

[0012] Through the imaging process, the data to be imaged are processed, and the target celestial body is imaged based on the processed data to be imaged, wherein, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain benchmark data to be imaged, the data to be imaged associated with the abnormal antenna and the benchmark data to be imaged are processed respectively, and the target celestial body is imaged based on the processed data to be imaged associated with the abnormal antenna and the processed benchmark data to be imaged.

[0013] Optionally, for each initial visibility data corresponding to the baseline, calibrating the initial visibility data corresponding to the baseline to obtain calibrated visibility data corresponding to the baseline specifically includes:

[0014] For the initial visibility data corresponding to each baseline, based on the antenna parameters corresponding to each antenna contained in the baseline and the amplitude information and phase information of the reference celestial body corresponding to the target celestial body, the initial visibility data corresponding to the baseline is calibrated to obtain the calibrated visibility data corresponding to the baseline, wherein, for each antenna, the antenna parameters corresponding to the antenna include the attribute information of the antenna itself and the delay information of the antenna in the process of collecting data.

[0015] Optionally, based on antenna parameters corresponding to each antenna included in the baseline and amplitude information and phase information of a reference celestial body corresponding to the target celestial body, initial visibility data corresponding to the baseline is calibrated to obtain calibrated visibility data corresponding to the baseline, specifically including:

[0016] Based on the average value of the autocorrelation spectrum corresponding to each antenna, the system temperature and the antenna gain temperature, the initial visibility data is amplitude calibrated to obtain the first-level calibration data;

[0017] A point-shaped celestial body meeting preset conditions is determined from other celestial bodies observed at the same time as the target celestial body as the reference celestial body, and a bandpass calibration is performed on the first-level calibration data according to the difference between the function of the amplitude and phase of each spectrum window of the reference celestial body in multiple frequency channels varying with the frequency and the benchmark function to obtain the second-level calibration data;

[0018] The phase of the second-level calibration data is calibrated according to the calculated delay and delay rate corresponding to each antenna to obtain the calibrated visibility data.

[0019] Optionally, determining the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline specifically includes:

[0020] For each calibrated visibility data corresponding to the baseline, the calibrated visibility data is divided into a plurality of data segments according to a preset time interval, and an average value of each data segment within the time interval is determined;

[0021] The calibrated visibility data is updated according to an average value of each data segment within the time interval to obtain updated visibility data;

[0022] According to the updated visibility data corresponding to each baseline, the data to be imaged for the target celestial body is determined.

[0023] Optionally, determining the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline specifically includes:

[0024] For each baseline, a weight factor corresponding to the baseline is determined according to the baseline parameters of the baseline;

[0025] According to the calibrated visibility data corresponding to each baseline and the weight factor corresponding to each baseline, each calibrated visibility data is rasterized to obtain the data to be imaged.

[0026] Optionally, it specifically includes:

[0027] Determining an initial pulse function according to the data to be imaged;

[0028] Performing a two-dimensional Fourier transform on the initial pulse function to obtain reference visibility data;

[0029] Correcting the data to be imaged according to the reference visibility data, and re-determining the reference visibility data according to the corrected data to be imaged;

[0030] The correction target is that the maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data is less than a preset brightness peak signal-to-noise ratio multiple, and the data to be imaged is corrected several times according to the re-determined reference visibility data to obtain the processed data to be imaged.

[0031] Optionally, taking the maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data as a correction target being less than a preset brightness peak signal-to-noise ratio multiple, performing several correction processes on the data to be imaged according to the re-determined reference visibility data to obtain the processed data to be imaged, specifically includes:

[0032] The maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data is less than a preset brightness peak signal-to-noise ratio multiple as a correction target, the corrected data to be imaged is corrected several times according to the re-determined reference visibility data, and the target pulse function is determined according to the data to be imaged obtained after the last correction;

[0033] Performing a two-dimensional Fourier transform on the target pulse function to obtain target reference visibility data;

[0034] According to the target reference visibility data, the amplitude normalization factor corresponding to each antenna is determined, with the goal of minimizing the error between the gain parameters corresponding to each antenna. The data to be imaged obtained after the last correction is corrected using the amplitude normalization factor to obtain the processed data to be imaged.

[0035] Optionally, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain reference data to be imaged, the data to be imaged associated with the abnormal antenna and the reference data to be imaged are processed respectively, and the target celestial body is imaged according to the processed data to be imaged associated with the abnormal antenna and the processed reference data to be imaged, which specifically includes:

[0036] Deleting the data to be imaged associated with the abnormal antenna from the data to be imaged to obtain reference data to be imaged;

[0037] Processing the reference data to be imaged to obtain first-level data to be imaged and fixing the first-level data;

[0038] Restoring the data to be imaged associated with the abnormal antenna in the first level of data to be imaged, and processing the data to be imaged associated with the abnormal antenna to obtain the second level of data to be imaged;

[0039] releasing the fixed first-level data to be imaged in the second-level data to be imaged to obtain target data to be imaged;

[0040] The target celestial body is imaged according to the target data to be imaged.

[0041] Optionally, imaging the target celestial body according to the processed data to be imaged specifically includes:

[0042] Performing a two-dimensional inverse Fourier transform on the data to be imaged to obtain a brightness distribution image, and determining a residual image according to a difference between the brightness distribution image and a preset pulse function;

[0043] Determining a peak point of the residual image in a specified area of ​​the brightness distribution image, and determining dirty beam data according to position information of the peak point and a preset loop gain value;

[0044] Determine correction data according to the dirty beam data and the preset pulse function, correct the residual image according to the difference between the data value at the peak point and the correction data, and update the preset pulse function according to the peak value in the updated residual image and the loop gain value;

[0045] Taking the minimization of the peak value in the updated residual image as the correction target, performing several corrections according to the updated impulse function and the updated residual image, and obtaining the correction value corresponding to each correction;

[0046] The brightness distribution image is reconstructed according to the correction value corresponding to each correction to obtain a target brightness distribution image for the target celestial body.

[0047] Optionally, before deleting the data to be imaged associated with the abnormal antenna from the data to be imaged through the imaging process, the method further includes:

[0048] According to the calibrated visibility data corresponding to each baseline, determining abnormal antennas and non-abnormal antennas among the antennas included in each baseline;

[0049] The data to be imaged that is associated with the abnormal antenna is determined from the data to be imaged.

[0050] Optionally, according to the calibrated visibility data corresponding to each baseline, determining abnormal antennas and non-abnormal antennas from antennas included in each baseline specifically includes:

[0051] For each baseline, according to the calibrated visibility data corresponding to the baseline, determine the amplitude information and phase information corresponding to each antenna included in the baseline as the separation information corresponding to each antenna included in the baseline;

[0052] Determine the average value and standard deviation of the separation information corresponding to each antenna included in each baseline;

[0053] For each antenna, a deviation value between the separation information corresponding to the antenna and the average value is determined; if the deviation is greater than a preset multiple of the standard deviation, the antenna is regarded as an abnormal antenna; otherwise, the antenna is regarded as a non-abnormal antenna.

[0054] Optionally, input parameters involved in the calibration process and the imaging process are calculated and acquired based on observation information matching the very long baseline interferometer array to adapt to visibility data processing of the baseline interferometer array.

[0055] This specification provides a celestial imaging device for very long baseline interferometry array data, comprising:

[0056] A receiving module, used to receive an imaging request for a target celestial body and start a calibration process according to the imaging request;

[0057] An acquisition module, used for acquiring the visibility data of the target celestial body collected by each baseline in a very long baseline interferometer array through the calibration process as initial visibility data corresponding to each baseline;

[0058] A calibration module, for calibrating the initial visibility data corresponding to each baseline to obtain calibrated visibility data corresponding to the baseline;

[0059] A determination module, used to determine the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, and start the imaging process;

[0060] An imaging module is used to process the data to be imaged through the imaging process, and to image the target celestial body according to the processed data to be imaged, wherein, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain benchmark data to be imaged, the data to be imaged associated with the abnormal antenna and the benchmark data to be imaged are processed respectively, and the target celestial body is imaged according to the processed data to be imaged associated with the abnormal antenna and the processed benchmark data to be imaged.

[0061] The present specification provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the celestial body imaging method based on a very long baseline interferometer array is implemented.

[0062] The present specification provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned celestial body imaging method based on a very long baseline interferometer array when executing the program.

[0063] At least one of the above technical solutions adopted in this specification can achieve the following beneficial effects:

[0064] In the celestial body imaging method based on a very long baseline interferometer array provided in the present specification, initial visibility data of the target celestial body observed through each baseline is obtained through a calibration process, and the initial visibility data is calibrated to obtain calibrated visibility data; based on each calibrated visibility data, the data to be imaged for the target celestial body is determined, and the imaging process is started; through the imaging process, the data to be imaged associated with the abnormal antenna is identified from the data to be imaged, so as to ensure that the data to be imaged related to the non-abnormal antenna and the data to be imaged associated with the abnormal antenna are processed separately while eliminating the interference of the abnormal antenna, and then the target celestial body is imaged according to the processed data.

[0065] It can be seen from the above method that in the process of imaging the target celestial body based on the visibility data collected from each baseline, this scheme can calibrate the initial visibility data directly obtained through each baseline through the calibration process, so as to avoid errors caused by internal and external reasons of the baseline, and realize automatic processing and automatic imaging of the imaging data through the imaging process. Compared with the current method in which R&D personnel manually process the visibility data, this scheme realizes the automatic processing and automatic imaging of the visibility data, improves the efficiency and accuracy of data processing in the process of celestial body imaging, and ensures the reliability of imaging quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] The drawings described herein are used to provide a further understanding of this specification and constitute a part of this specification. The illustrative embodiments and descriptions of this specification are used to explain this specification and do not constitute an improper limitation on this specification. In the drawings:

[0067] Figure 1 A schematic diagram of a flow chart of a celestial body imaging method for very long baseline interferometry array data provided in this specification;

[0068] Figure 2 A schematic diagram of the overall process of calibrating visibility data provided in this specification;

[0069] Figure 3 A schematic diagram of the overall process of imaging a target celestial body provided in this specification;

[0070] Figure 4 A schematic diagram of the imaging result of a target celestial body provided in this specification;

[0071] Figure 5 A schematic diagram of a celestial imaging device for very long baseline interferometric array data provided in this specification;

[0072] Figure 6 A method corresponding to the Figure 1 Schematic diagram of electronic equipment. DETAILED DESCRIPTION

[0073] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0074] The technical solutions provided by the embodiments of this specification are described in detail below in conjunction with the accompanying drawings.

[0075] Figure 1 The present invention provides a flow chart of a method for celestial body imaging based on very long baseline interferometry array data, which includes the following steps:

[0076] S101: receiving an imaging request for a target celestial body, and starting a calibration process according to the imaging request.

[0077] S102: Obtaining, through the calibration process, visibility data of the target celestial body collected by each baseline in a very long baseline interferometer array as initial visibility data corresponding to each baseline.

[0078] Very Long Baseline Interferometry (VLBI), as a radio astronomy technique for high-resolution observation of celestial bodies, has been widely used in many key scientific fields such as deep space lunar exploration, satellite exploration, black hole detection, and astrophysical research. For example, by using VLBI technology, the Event Horizon Telescope (EHT) successfully combined radio telescopes distributed around the world into a giant virtual telescope with an aperture equivalent to the diameter of the earth. This feat not only captured the image of a black hole for the first time, but also enabled the EHT project team to win the 2020 Breakthrough Prize in Fundamental Physics.

[0079] Recently, researchers from 45 research institutions around the world have made a major discovery in black hole research using multiple VLBI networks: the jet from the black hole at the center of the M87 galaxy shows periodic oscillations, with a period of about 11 years and an amplitude of about 10 degrees. This discovery proves Einstein's general theory of relativity's prediction that a black hole will have a dragging effect when it is rotating. These successes in recent years have fully demonstrated the unique advantages of millimeter-wave VLBI technology in exploring the mysteries of the universe and studying supermassive black holes. However, these discoveries all require processing a large amount of observational data and statistically analyzing the processed results.

[0080] Therefore, in order to further improve the efficiency of processing observation data and the reliability of imaging results, this specification provides a method for astronomical imaging of very long baseline interferometric array data, first designing and implementing each key step of data processing, and then integrating these independent data processing steps into a pipeline system. In this way, the operating efficiency of data processing is significantly improved, and it has the ability to automatically process large quantities of data. In addition, the present invention also programs and codes the manual interactive processing steps to avoid manual human errors.

[0081] In the present specification, the execution entity for executing a celestial body imaging method for very long baseline interferometer array data can be a designated device such as a server. For the sake of ease of description, the following will only take the server as the execution entity as an example to illustrate a celestial body imaging method for very long baseline interferometer array data provided in this specification.

[0082] The server may receive an imaging request for a target celestial body, and then initiate a calibration process based on the imaging request to execute a data processing script.

[0083] Afterwards, the server can obtain the visibility data of the target celestial body collected by each baseline in the very long baseline interferometer array at different times through the calibration process.

[0084] In very long baseline interferometry observations, antennas (astronomical telescopes) are distributed at different geographical locations. Each pair of antennas forms a baseline. The data generated by observations of different baselines cover different areas in Fourier space. The visibility data collected by each baseline in the very long baseline interferometry array can be recorded in a (Flexible Image Transport System-Interferometry Data Interchange, FITS-IDI) format file, which stores radio interferometry measurement data and its related calibration data as a series of binary tables.

[0085] In this specification, the above very long baseline interferometer array can be the East Asian VLBI network, which is composed of radio telescopes in China, South Korea and Japan, of which 4 are from China, 3 are from South Korea, and 12 are from Japan. Of course, it can also be a VLBI network in other regions, and this specification does not make specific restrictions on this.

[0086] In this specification, the server can use the task number variable Select the starting step of the calibration process to facilitate debugging of key steps with new data.

[0087] Among other things, the calibration process can perform one or more of the following tasks:

[0088] Task (1): Initialize the various parameter settings designed during data processing, including , observation code name, reference antenna name, name of reference celestial body in bandpass calibration, name of phase reference celestial body, name of target celestial body, FITS-IDI file, data input and output path, frequency number, name of celestial body to be removed in phase calibration, name of abnormal antenna, where It can be a natural number from 1 to 7.

[0089] For example, the observation code name can be set to a17078a, the reference antenna name can be set to KUS, the name of the reference celestial body in the bandpass calibration can be set to 3C273, the name of the phase reference celestial body can be set to 3C273, the name of the target celestial body can be set to M87, the FITS-IDI file can be set to a17078a.fits, the data input and output path can be set to the data file under the program path, the frequency number can be set to 1, the name of the celestial body to be removed in the phase calibration can be set to RT-VIR, and the name of the abnormal antenna can be set to TIA.

[0090] Task (2): Based on The value of determines the task that the calibration process currently needs to perform, where if Then execute task (3) if Then execute task (4) if Then execute task (5) if Then execute task (6) if Then execute task (7) if Then execute task (8) if Then execute task (9); if If the number is not a natural number between 1 and 7, no data processing task will be performed.

[0091] Task (3): Read the visibility data of the target celestial body in the FITS-IDI file and store it in the server hard disk. The data is marked as UVDATA. Then, the calibration table (CL) time interval is set to 1 / 60 min.

[0092] For example, the server can read the data corresponding to the target object M87 in the a17078a.fits file and store it in the hard disk through the calibration process.

[0093] Task (4): Read UVDATA from the hard disk, obtain the data sorting mark and determine whether it is 'TB', that is, whether it is sorted data. If it is not 'TB', sort the UVDATA data according to the time and baseline number sequence, and store the sorted data in the hard disk, and mark the data as MSORTDATA; if the data sorting mark is 'TB', directly change UVDATA to MSOTDATA. Based on this data, establish the first CL1 table and the first new index (Newindex, NX) table (NX1) at 1 / 60 min time intervals.

[0094] After tasks (1) to (4), the server can obtain the visibility data of the target celestial body collected by each baseline in the very long baseline interferometer array at different times as the initial visibility data (i.e., CL1).

[0095] Task (5): Read MSORTDATA from the hard disk and view the data. Check the number of observation segments, the number of celestial bodies, the number of observation frequencies, the number of intermediate frequency channels (IF), the bandwidth of each IF, the bandwidth of the spectral channel, the start and end time of the observation, the data length, the integration time, and the length of the visibility data on each interferometric baseline, and output this information to the log file.

[0096] Here we take a17078a as an example. Its corresponding MSORTDATA data includes 33 observation frequency bands, 5 celestial bodies (3C273, M87, M84, 1219+044 and RT-VIR), 1 observation frequency (43.006GHz), 8 intermediate frequency channels (IF), the bandwidth of each IF is 32MHz, the bandwidth of the spectral channel is 125kHz, the observation start and end time (0 / 11:40:00, 0 / 18:39:57), the data length is 770197, the integration time is 0.8192s, and the length of the visibility data on each interferometric baseline is 2751.

[0097] S103: for each baseline corresponding to the initial visibility data, calibrate the initial visibility data corresponding to the baseline to obtain calibrated visibility data corresponding to the baseline.

[0098] Due to the distance between telescopes, frequency drift, receiver noise, delay and other factors between baselines, there will be deviations in the amplitude and phase of the observation data on different baselines. In order to synthesize the image, it is first necessary to calibrate the initial visibility data of each baseline, including amplitude calibration (adjusting the signal strength to a consistent scale) and phase calibration (correcting the phase delay of the signal to compensate for the time difference between antennas and the phase offset of the antennas).

[0099] For each initial visibility data, the calibration process can calibrate the initial visibility data based on the antenna parameters corresponding to each antenna contained in the baseline corresponding to the initial visibility data, the amplitude information and phase information of the reference celestial body corresponding to the target celestial body, and obtain the calibrated visibility data corresponding to the initial visibility data, wherein for each antenna, the antenna parameters corresponding to the antenna include the attribute information of the antenna itself and the delay information of the antenna in the data collection process, and the attribute information of the antenna itself may include the system temperature of the antenna , Gain Temperature and autocorrelation spectrum data.

[0100] In practical applications, antenna system temperature refers to the total noise power received by the antenna receiving system within a unit bandwidth. Its unit is K, which includes the noise temperature of the antenna itself and the noise temperature generated by other components in the system (such as the low noise amplifier LNA).

[0101] Antenna gain temperature (G / T) indicates the ratio of antenna gain to system noise temperature, expressed in dB / K. The larger the G / T value, the better the performance of the ground station receiving system.

[0102] First, in order to ensure that the measurement results of different antennas are physically consistent and that the intensity values ​​in the image have real physical meanings, the calibration process can perform amplitude calibration on the initial visibility data based on the average value of the autocorrelation spectrum corresponding to each antenna, the system temperature, and the antenna gain temperature to obtain the first-level calibration data;

[0103] Next, in order to correct the frequency response of the antenna and receiver over the entire observation frequency band and ensure the gain and phase consistency between each frequency channel, the calibration process can perform a bandpass calibration on the first-level calibration data. The calibration process can determine a point-like celestial body that meets the preset conditions among other celestial bodies observed at the same time as the target celestial body, as a reference point-like celestial body, and perform a bandpass calibration on the first-level calibration data based on the difference between the function of the amplitude and phase of each spectrum window of the reference point-like celestial body in multiple frequency channels and the reference function as the frequency changes, to obtain the second-level calibration data, wherein the above-mentioned preset conditions may include: the distance from the target celestial body is less than the preset distance, reaches the preset brightness, etc. Of course, other conditions may also be included in practical applications, and this manual does not specifically limit this;

[0104] Afterwards, in order to eliminate the phase deviation between the antennas and ensure coherent imaging, the calibration process can perform phase calibration on the second-level calibration data, and then calibrate the second-level calibration data according to the calculated delay and delay rate corresponding to each antenna to obtain the calibrated visibility data.

[0105] Specifically, the calibration process can be implemented by performing the following tasks, including:

[0106] Task (6): Perform amplitude calibration on CL1 to obtain CL3 (first-level calibration data). In practical applications, the initial visibility data obtained by observing the target celestial body with a very long baseline array can be expressed as:

[0107]

[0108] in, represents the calibrated visibility data observed by the baseline formed by antenna i and antenna j, represents the error factor caused by the digital receiver, and represents the system temperature of the antenna, and is the antenna gain temperature.

[0109] Since the visibility data is recorded using a digital receiver for analog-to-digital conversion, the amplitude information of the data is lost in the process, resulting in an amplitude deviation between the measured data and the actual data. Therefore, the calibration process can determine the mean of the autocorrelation spectrum data of each antenna to obtain the above-mentioned error factor b, and record these autocorrelation spectrum averages in the first calibration solution (Solution, SN) table to obtain SN1.

[0110] Subsequently, the calibration process may interpolate the SN1 table to CL1 by a self-interpolation method, thereby updating CL1 to obtain CL2. The calibration process may then further perform calibration for the antenna system temperature and gain temperature.

[0111] The antenna system temperature and antenna gain temperature are recorded in an antab file. The calibration process can first read the antenna system temperature and gain temperature from this antab file and record them as (Tsys, TY) table and (Gain Curve, GC) table. Then use the TY table and GC table to generate the SN2 table containing amplitude correction information. Finally, SN2 is interpolated to CL2 to update it, and CL3 is obtained, which is used as the first-level calibration data.

[0112] Of course, in practical applications, the initial visibility data may also be amplitude calibrated according to other parameters, such as the autocorrelation information of each antenna and the cross-correlation information between different antennas, etc. This specification does not specifically limit this.

[0113] Task (7): Perform bandpass calibration on the first-level calibration data (CL3) to obtain the second-level calibration data (CL4).

[0114] During the entire process of celestial body observation, the frequency passband of all electronic equipment cannot be ideal. The signals received by different telescopes in the array have inconsistent spectral shapes, that is, there is a bandpass effect. This effect will affect the image quality in the subsequent imaging steps, so bandpass correction is required.

[0115] Specifically, since brighter celestial bodies do not have obvious spectral characteristics (i.e., the true phase-frequency function is 0, and the amplitude-frequency function is a constant), and its model is easy to construct (i.e., point or circular Gaussian), the calibration process can identify point-like celestial bodies with brightness higher than a preset value among other celestial bodies observed at the same time as the target celestial body as reference point-like celestial bodies. The preset value can be set according to actual conditions, and this manual does not make specific limitations on this.

[0116] After that, the calibration process can calculate the difference between the amplitude and phase of each spectrum window of the multiple frequency channels of the point-like celestial body as a function of frequency and the ideal true function, and calculate the solution within the entire frequency bandpass range. Finally, this bandpass calibration solution is applied to the amplitude and phase data of the corresponding spectrum window of the target celestial body for correction and recorded as a bandpass calibration solution table (BP table).

[0117] The calibration process can use the interpolation method to update CL3 through the BP table to obtain CL4, which is used as the second-level calibration data.

[0118] Task (8): Perform phase calibration on the second-level calibration data (CL4) to obtain (CL5). The calibration process can correct the signal time difference and phase difference between telescopes by calculating the delay and delay rate of the antenna. In the process of array observation of celestial bodies, there is usually uncertainty in the position of the target celestial body or the antenna in the array. The errors in the earth model, the delay when the celestial body passes through the atmosphere, the clock differences between the telescopes, the phase residue and residual rate between different frequency channels during the data reception and processing, and the refraction effect when the celestial body signal passes through the ionosphere, all these factors work together to cause different degrees of time and phase errors in the celestial body signal.

[0119] The calibration process can read the antenna from the MSORTDATA on the hard disk. and antenna j In time t Time and frequency v The visibility data at this time can be expressed in plural form:

[0120]

[0121] in, The true phase of the target object, The phase error introduced by various factors can be expressed as:

[0122]

[0123] in, and At the reference time and frequency The phase error at and is the delay or residual delay, and is the delay rate, and Indicates the difference between time and frequency relative to a reference value. , Phase calibration is to remove the phase error factor The present invention adopts a global fringe fitting method to solve the phase, delay and delay rate, and then the optimal parameters of the model can be solved based on the least squares fitting method to perform phase calibration. The constructed model is:

[0124]

[0125] in, yes The conjugate function of is the weight function, is the unknown parameter vector In the fitting process, the telescope with the middle number is selected as the reference antenna, and the confidence (signal-to-noise ratio) is set to 5. After obtaining the solution of the above model, it can be recorded as the SN3 table. The two points of the SN3 table are interpolated to CL4 to update it, thereby obtaining CL5, which is used as the visibility data after calibration.

[0126] The calibration process can correct the observed visibility collected from each baseline by executing tasks (6) to (8), thereby obtaining the calibrated visibility data.

[0127] The server may then perform the following steps to store and manage the data determined during the calibration process.

[0128] Task (9): The CL, SN, and BP tables generated in the above steps are stored in MSORTDATA, which contains multiple celestial bodies. In order to map each celestial body individually, the calibration process can separate the data belonging to the specified celestial body and generate multiple independent FITS format files. Here, the MSORTDATA data with the CL5, SN3, and BP1 tables applied is divided into multiple parts according to different celestial bodies and stored in the hard disk according to the names of the celestial bodies. The data is then read from the hard disk, written into different FITS files according to the original format of MSORTDATA, and output.

[0129] Taking a17078a as an example, the final output files can be expressed as: a17078a_3C273.UVDATA.FITS, a17078a_M87.UVDATA.FITS, a17078a_M84.UVDATA.FITS, a17078a_1219+044.UVDATA.

[0130] FITS and a17078a_RT-VIR.UVDATA.FITS. For ease of understanding, this manual provides a schematic diagram of the overall process of calibrating visibility data, such as Figure 2 shown.

[0131] Figure 2 The figure is a schematic diagram of the overall process of calibrating visibility data provided in this specification.

[0132] The calibration process can be based on The initial value of the calibration process is used to perform data loading, data sorting, data viewing, amplitude calibration, bandpass calibration, phase calibration and data separation tasks. To update (i.e. ), so that according to the updated Perform the next task.

[0133] It should be pointed out that in actual applications, the calibration process can also calibrate the initial visibility data through other known calibration methods (such as predicting the actual visibility data based on the input initial visibility data through a pre-trained deep learning model) or other calibration sequences (such as in the order of amplitude calibration, phase calibration, and bandpass calibration), and this manual does not make specific limitations on this.

[0134] S104: Determine the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, and start the imaging process.

[0135] In order to reduce the amount of data used in the imaging process of the target celestial body, improve imaging efficiency and signal-to-noise ratio, and reduce computational complexity, for each calibrated visibility data, the server can divide the calibrated visibility data into several data segments according to a preset time interval, and determine the average value of each data segment within the time interval, and update the calibrated visibility data according to the average value of each data segment within the time interval to obtain updated visibility data, and then determine the data to be imaged for the target celestial body according to each updated visibility data.

[0136] In an embodiment provided in the present specification, the server can read the actual visibility data, UU, VV and time in the 0th header and data unit (HDU) of the target celestial body FITS file, divide the calibrated visibility data into intervals of 30 seconds, calculate the average value of the data within each 30 seconds to obtain new data points, and overwrite the original data with the new data points.

[0137] In addition, the server can make preliminary corrections to the updated visibility data, use a two-dimensional circular Gaussian model for a rough estimate, perform a two-dimensional Fourier transform on the model to obtain visibility data corresponding to the model, and use the visibility data of the model as a reference to correct the time-averaged visibility data, and then clear the model.

[0138] Furthermore, the server may also perform rasterization processing on the updated visibility data according to each calibrated visibility data and a preset weight factor, so as to obtain the data to be imaged.

[0139] The server can set the image size required for the target celestial body to be mapped. and the rasterized cell size Because the visibility data obtained by observation is not a strict two-dimensional matrix, it needs to be digitally rasterized and converted into a two-dimensional matrix before mapping. The visibility data within is accumulated and multiplied by a weight factor. In this specification, the weight factor corresponding to each baseline can be determined based on the baseline parameters of each baseline, which may include the baseline's signal-to-noise ratio, the antenna's receiving sensitivity, and the baseline length, etc.

[0140] Of course, the weight factor can be a uniform weight factor, that is ,in It is The amount of visibility within the grid cell.

[0141] It should be noted that different weighting strategies can optimize the image resolution and signal-to-noise ratio. For example, natural weights can improve the signal-to-noise ratio, while uniform weights can improve resolution. Weighting can reduce the impact of sparse sampling when mapping, making the image distribution more uniform.

[0142] After determining the data to be imaged, the server can start the imaging process.

[0143] It should be noted that in actual applications, the server may not update the calibrated visibility data, but directly perform weighted processing on each calibrated visibility data to obtain the data to be imaged.

[0144] S105: Through the imaging process, the data to be imaged are processed, and the target celestial body is imaged according to the processed data to be imaged, wherein, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain benchmark data to be imaged, the data to be imaged associated with the abnormal antenna and the benchmark data to be imaged are processed respectively, and the target celestial body is imaged according to the processed data to be imaged associated with the abnormal antenna and the processed benchmark data to be imaged.

[0145] The server can perform imaging tasks for the target celestial body through the imaging process.

[0146] Since there is a residual phase error that has not been eliminated in the process of correcting the initial visibility data, if this error is not eliminated, some wrong results and negative components will be generated during the deconvolution process. Therefore, the imaged data can be self-calibrated during the imaging process to eliminate these errors.

[0147] Specifically, the server can perform deconvolution processing on the imaged data for a specified number of iterations to obtain an initial pulse function, and then perform a two-dimensional Fourier transform on the initial pulse function to obtain reference visibility data, and then perform correction processing on the imaged data according to the reference visibility data, and redetermine the reference visibility data based on the corrected imaged data. The imaging process can use the maximum value of the residual map between the corrected imaged data and the redetermined reference visibility data as a correction target that is less than a preset brightness peak signal-to-noise ratio multiple, and perform several corrections on the imaged data according to the redetermined reference visibility data to obtain the target imaged data.

[0148] Among them, the imaging process can construct a simple model of the celestial body based on the data to be imaged, that is, through the deconvolution method, a pulse function is obtained as a model with a smaller number of iterations, and the pulse function is transformed into reference visibility data by two-dimensional Fourier transform, and the reference visibility data is used to correct the data to be imaged; the calibrated visibility data is used for deconvolution to obtain a more accurate model and clean image. Then the visibility data is calibrated according to the new model. The self-calibration is performed cyclically until the maximum value of the residual map between the visibility data and the pulse function is less than the preset peak signal-to-noise ratio multiple The pulse function generated in each iteration of this process will be used as a temporary model, and the temporary models of each iteration will eventually be accumulated into a permanent model. The self-calibration in this step only calibrates the phase of the visibility data.

[0149] After obtaining the data to be imaged, the imaging process can image the target celestial body based on the data to be imaged.

[0150] Imaging the target celestial body is to obtain the true brightness distribution function diagram of the celestial body. The observation field of the East Asian Very Long Baseline Interferometer Array is small, and the sky area of ​​the target celestial body can be regarded as a two-dimensional plane. At this time, the visibility data obtained by observation and the true brightness distribution function of the celestial body are in a two-dimensional Fourier transform relationship, so in theory, the brightness distribution function can be obtained by performing a two-dimensional inverse Fourier transform on the visibility data. However, due to the limited number of arrays, its sampling of the sky area of ​​the celestial body is incomplete, and there are large holes. The brightness distribution function obtained by directly performing a two-dimensional inverse Fourier transform is a dirty image. Therefore, this scheme can use a deconvolution algorithm to clean the dirty image and restore the true brightness distribution function, that is, the target image.

[0151] Specifically, the server can perform a two-dimensional inverse Fourier transform on the imaging data through the imaging process to obtain a brightness distribution image (i.e., a dirty image), and determine a residual image according to the difference between the brightness distribution image and a preset pulse function. Then, the peak point of the residual image is determined in a specified area of ​​the brightness distribution image, and the dirty beam data is determined according to the position information of the peak point and a preset loop gain value, and then the correction data is determined according to the dirty beam data and the preset pulse function, and the residual image is corrected according to the difference between the data value at the peak point and the correction data, and the preset pulse function is updated according to the peak value and the loop gain value in the updated residual image.

[0152] The imaging process can take the peak value minimization in the updated residual image as the correction target, perform several corrections according to the updated pulse function and the updated residual image, and obtain the corresponding correction value at each correction. Finally, the brightness distribution image is reconstructed according to the corresponding correction value at each correction to obtain the target brightness distribution image for the target celestial body.

[0153] Among them, in the process of deconvolution, the imaging process can first initialize the image size to be imaged as , the pixel resolution is ; Then, the peak point in the residual graph is found in the set rectangular area of ​​the image. The server can pre-set a loop gain value ( ), where the residual image is obtained by subtracting the impulse function from the dirty image, wherein the initial impulse function is a matrix whose elements are all 0. In practical applications, the above loop gain value can be a floating point number of 0-1, and its specific value can be set according to actual conditions, and this specification does not make specific limitations on this.

[0154] Afterwards, the dirty bundle data (i.e. and The center of the two-dimensional Fourier transform of the two-dimensional matrix composed of the two-dimensional Fourier transform multiplied by the loop gain value is placed at the peak point, and the product of the dirty beam and the pulse function is subtracted at this point in the residual image to obtain a new residual image, where the pulse function can be a peak value The imaging process can record the position of the peak and the amount of subtraction, and repeat the above operation until the maximum value of the final residual image is less than the set threshold or the iteration ends. Finally, the subtracted component is reconstructed, that is, the impulse response of the subtracted position is calculated, and the final impulse function is convolved with the clean beam (a two-dimensional Gaussian of the same size as the dirty beam) to obtain a brightness distribution image of the target celestial body.

[0155] In addition, during the observation process, different antennas have different antenna parameters such as location, equipment sensitivity, and environmental factors, which will lead to inconsistent received signal strength and gain parameters. This inconsistent gain (i.e., the amplification factor of the antenna receiving signal) difference will affect the amplitude information in the data, thus having a negative impact on the accuracy of the map and the final effect of the image.

[0156] Therefore, in the process of processing the data to be imaged, the imaging process can adjust the gain of all antennas to a consistent level by calculating the relative difference of each antenna gain to achieve antenna gain calibration. Among them, the imaging process can determine the target pulse function based on the data to be imaged obtained after the last calibration, and then perform a two-dimensional Fourier transform on the target pulse function to obtain the target reference visibility data, and then determine the amplitude normalization factor corresponding to each antenna based on the target reference visibility data, and minimize the error between the gain parameters corresponding to each antenna. The data to be imaged obtained after the last calibration is corrected by the amplitude normalization factor to obtain the target data to be imaged. After that, the imaging process can once again perform self-calibration processing on the target data to be imaged.

[0157] In addition, the server can also self-calibrate the amplitude and phase of the target data to be imaged. Based on the above self-calibration method, the target amplitude and phase are calibrated, and different integration intervals of 120, 60, 20, 10, 6, 3, 1, and 0.5 minutes are used in turn to obtain the calibration solution for self-calibration. Each self-calibration performs a temporary model clearing and permanent pulse function accumulation. At the same time, each self-calibration is combined with a deconvolution.

[0158] Furthermore, since some abnormal antennas often appear due to internal and external reasons in the process of observing data of the very long baseline interferometer array, in order to avoid the influence of these abnormal antennas on the final imaging results, in the process of processing the imaging data, the imaging process can determine the abnormal antennas and non-abnormal antennas in each baseline of the very long baseline interferometer array.

[0159] For each baseline, the server can determine the amplitude information and phase information corresponding to each antenna contained in the baseline based on the calibrated visibility data corresponding to the baseline, as the separation information corresponding to each antenna contained in the baseline, and then determine the average value and standard deviation of the separation information corresponding to each antenna contained in each baseline. For each antenna, determine the deviation value between the separation information corresponding to the antenna and the average value; if the deviation is greater than a preset multiple of the standard deviation, the antenna is regarded as an abnormal antenna; otherwise, the antenna is regarded as a non-abnormal antenna.

[0160] In an embodiment provided in the present specification, the server can obtain the visibility data corresponding to the updated celestial body with stronger brightness obtained after the data is time-averaged, and read the amplitude and phase arrays, the arrays of the baseline 1 name ant1 and the baseline 2 name ant2, and the names of all antennas ant_all. Then, the antenna name ant in ant_all is traversed, the index of ant appearing in the ant1 and ant2 arrays is calculated, the two groups of indexes are merged and the duplication is removed to obtain the final ant index ant_index, and then the amplitude and phase under the index ant_index in amp and phase are obtained as the amplitude and phase of the antenna ant, and so on, to obtain the amplitude and phase data of all antennas. Finally, the average value and standard deviation of the amplitude and phase of these different antennas are calculated respectively, and the deviation of the amplitude and phase of each antenna is calculated with this average value as a reference. If the maximum deviation value exceeds 3 times the standard deviation, the antenna name corresponding to the maximum deviation value will be identified as an abnormal antenna, and the remaining antennas except the abnormal antenna will be non-abnormal antennas.

[0161] In order to prevent the influence of non-abnormal antennas on the processing results during the processing of the data to be imaged, the imaging process can determine the data to be imaged associated with the abnormal antenna in the data to be imaged, and delete the data to be imaged associated with the abnormal antenna in the data to be imaged to obtain the benchmark data to be imaged, and then perform the above processing (including self-calibration and antenna gain calibration) on the data to be imaged associated with the abnormal antenna and the benchmark data to be imaged respectively, and image the target celestial body according to the processed data to be imaged associated with the abnormal antenna and the processed benchmark data to be imaged.

[0162] Specifically, the imaging process can first delete the data to be imaged associated with the abnormal antenna from the data to be imaged, obtain the reference data to be imaged that only contains the data corresponding to the non-abnormal antenna, and then process the reference data to be imaged to obtain the first-level data to be imaged and fix it.

[0163] The imaging process can then recover the data to be imaged associated with the abnormal antenna in the first level of data to be imaged, and process the data to be imaged associated with the abnormal antenna (since the first level of data to be imaged has been fixed, this step will only process the data to be imaged associated with the abnormal antenna) to obtain the second level of data to be imaged.

[0164] The imaging process can release the fixed first-level data to be imaged in the second-level data to be imaged to obtain target data to be imaged. At this time, the target data to be imaged includes the data to be imaged associated with the abnormal antenna after independent processing and the data to be imaged associated with the non-abnormal antenna after independent processing.

[0165] Finally, the imaging process can further perform self-calibration and deconvolution processing on the target data to be imaged, thereby obtaining the imaging result (i.e., brightness distribution image) for the target celestial body.

[0166] For ease of understanding, this manual provides a schematic diagram of the overall process of imaging a target celestial body, such as Figure 3 shown.

[0167] Figure 3 This is a schematic diagram of the overall process of imaging a target celestial body provided in this specification.

[0168] Among them, the server can perform time averaging on the imaging data to obtain updated data, and then the server can perform preliminary correction, abnormal antenna marking, weighting, self-calibration, overall amplitude self-calibration, self-calibration again, amplitude phase self-calibration, amplitude fixed self-calibration, full antenna self-calibration and deconvolution processing on it in sequence to obtain the final imaging result.

[0169] In addition, this manual also provides a schematic diagram of the imaging results of a target celestial body, such as Figure 4 shown.

[0170] Figure 4 This is a schematic diagram of the imaging results of a target celestial body provided in this specification.

[0171] The strong intensity area and contour outline in the figure are the imaging results of the target celestial body M87. The surrounding area has less noise and lower intensity. The structural morphology of the entire M87 is consistent with the calibrated M87 imaging result at the same frequency. It shows that the method of the present invention is effective and correct. The peak intensity of M87 is 0.95 Jansky / beam, and the background noise level of the imaging result is 0.25 milli-Jansky / beam. The lower right corner of the figure is the beam, with a major semi-axis length of 0.84 milliarcseconds, a minor semi-axis length of 0.55, a position angle of -16 degrees, and a dynamic range (peak intensity / background noise level) of 3784, which is 1.5 times higher than the existing method. The analysis data of these imaging results show that the celestial body imaging results obtained by the method of the present invention are more accurate.

[0172] It should be noted that since the information and parameters of different very long baseline interferometer arrays are different, in order to ensure that the visibility data processing and the imaging process are adapted to the current very long baseline interferometer array, the input parameters involved in the calibration process and the imaging process are calculated and acquired based on the observation information that matches the very long baseline interferometer array, so as to adapt to the visibility data processing of the baseline interferometer array.

[0173] From the above method, it can be seen that this scheme can obtain accurate and complete celestial structure mapping results, which is of great significance for subsequent scientific research. All steps do not require manual operation and are all implemented using program code, which can meet the needs of large-scale data processing. In addition, each step is carried out according to the dynamic setting of input parameters of different interferometer arrays, and can process observation data of other mainstream very long baseline interferometer arrays.

[0174] The above is one or more embodiments of the present specification of a celestial body imaging method based on a very long baseline interferometer array. Based on the same idea, the present specification also provides a corresponding celestial body imaging device based on a very long baseline interferometer array, such as Figure 5 shown.

[0175] Figure 5 A schematic diagram of an astronomical imaging device for very long baseline interferometry array data provided in this specification includes:

[0176] A receiving module 501 is used to receive an imaging request for a target celestial body and start a calibration process according to the imaging request;

[0177] An acquisition module 502 is used to acquire the visibility data of the target celestial body collected by each baseline in the very long baseline interferometer array through the calibration process as initial visibility data corresponding to each baseline;

[0178] A calibration module 503, for calibrating the initial visibility data corresponding to each baseline to obtain calibrated visibility data corresponding to the baseline;

[0179] A determination module 504 is used to determine the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, and start an imaging process;

[0180] The imaging module 505 is used to process the data to be imaged through the imaging process, and to image the target celestial body according to the processed data to be imaged, wherein, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain the benchmark data to be imaged, and the data to be imaged associated with the abnormal antenna and the benchmark data to be imaged are processed respectively, and the target celestial body is imaged according to the processed data to be imaged associated with the abnormal antenna and the processed benchmark data to be imaged.

[0181] Optionally, the calibration module 503 is specifically used to calibrate the initial visibility data corresponding to each baseline based on antenna parameters corresponding to each antenna contained in the baseline, and amplitude information and phase information of a reference celestial body corresponding to the target celestial body, to obtain calibrated visibility data corresponding to the baseline, wherein, for each antenna, the antenna parameters corresponding to the antenna include attribute information of the antenna itself and delay information of the antenna during data collection.

[0182] Optionally, the calibration module 503 is specifically configured to perform amplitude calibration on the initial visibility data based on an average value of an autocorrelation spectrum corresponding to each antenna, a system temperature, and an antenna gain temperature to obtain first-level calibration data;

[0183] A point-shaped celestial body meeting preset conditions is determined from other celestial bodies observed at the same time as the target celestial body as the reference celestial body, and a bandpass calibration is performed on the first-level calibration data according to the difference between the function of the amplitude and phase of each spectrum window of the reference celestial body in multiple frequency channels varying with the frequency and the benchmark function to obtain the second-level calibration data;

[0184] The phase of the second-level calibration data is calibrated according to the calculated delay and delay rate corresponding to each antenna to obtain the calibrated visibility data.

[0185] The determination module 504 is specifically used to divide the calibrated visibility data corresponding to each baseline into a plurality of data segments according to a preset time interval, and determine the average value of each data segment within the time interval; update the calibrated visibility data according to the average value of each data segment within the time interval to obtain updated visibility data; and determine the data to be imaged for the target celestial body according to the updated visibility data corresponding to each baseline.

[0186] Optionally, the determination module 504 is specifically used to determine, for each baseline, a weight factor corresponding to the baseline according to the baseline parameters of the baseline; and to rasterize each calibrated visibility data according to the calibrated visibility data corresponding to each baseline and the weight factor corresponding to each baseline to obtain the data to be imaged.

[0187] Optionally, the imaging module 505 is specifically used to determine an initial pulse function based on the data to be imaged; perform a two-dimensional Fourier transform on the initial pulse function to obtain reference visibility data; perform correction processing on the data to be imaged based on the reference visibility data, and redetermine the reference visibility data based on the corrected data to be imaged; take the maximum value of the residual map between the corrected data to be imaged and the redetermined reference visibility data as a correction target being less than a preset brightness peak signal-to-noise ratio multiple, perform several correction processing on the data to be imaged based on the redetermined reference visibility data, and obtain the processed data to be imaged.

[0188] Optionally, the imaging module 505 is specifically used to take the maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data as a correction target being less than a preset brightness peak signal-to-noise ratio multiple, perform several corrections on the corrected data to be imaged according to the re-determined reference visibility data, and determine a target pulse function based on the data to be imaged obtained after the last correction; perform a two-dimensional Fourier transform on the target pulse function to obtain target reference visibility data; determine an amplitude normalization factor corresponding to each antenna according to the target reference visibility data, take minimizing the error between the gain parameters corresponding to each antenna as the goal, perform correction processing on the data to be imaged obtained after the last correction using the amplitude normalization factor, and obtain the processed data to be imaged.

[0189] Optionally, the imaging module 505 is specifically used to delete the data to be imaged associated with the abnormal antenna in the data to be imaged to obtain benchmark data to be imaged; process the benchmark data to be imaged to obtain and fix the first-level data to be imaged; restore the data to be imaged associated with the abnormal antenna in the first-level data to be imaged, and process the data to be imaged associated with the abnormal antenna to obtain second-level data to be imaged; release the fixed first-level data to be imaged in the second-level data to be imaged to obtain target data to be imaged; and image the target celestial body according to the target data to be imaged.

[0190] Optionally, the imaging module 505 is specifically used to perform a two-dimensional inverse Fourier transform on the data to be imaged to obtain a brightness distribution image, and to determine a residual image based on the difference between the brightness distribution image and a preset pulse function; to determine a peak point of the residual image within a specified area of ​​the brightness distribution image, and to determine dirty beam data based on the position information of the peak point and a preset loop gain value; to determine correction data based on the dirty beam data and the preset pulse function, and to correct the residual image based on the difference between the data value at the peak point and the correction data, and to update the preset pulse function based on the peak value in the updated residual image and the loop gain value; to take minimizing the peak value in the updated residual image as the correction target, to perform several corrections based on the updated pulse function and the updated residual image, and to obtain a correction value corresponding to each correction; to reconstruct the brightness distribution image based on the correction value corresponding to each correction, and to obtain a target brightness distribution image for the target celestial body.

[0191] Optionally, through the imaging process, before deleting the data to be imaged associated with the abnormal antenna from the data to be imaged, the determination module 504 is also used to determine the abnormal antennas and non-abnormal antennas among the antennas included in each baseline based on the calibrated visibility data corresponding to each baseline; and determine the data to be imaged associated with the abnormal antenna from the data to be imaged.

[0192] Optionally, input parameters involved in the calibration process and the imaging process are calculated and acquired based on observation information matching the very long baseline interferometer array to adapt to visibility data processing of the baseline interferometer array.

[0193] This specification also provides a computer-readable storage medium, which stores a computer program, which can be used to execute the above Figure 1 A celestial imaging method for very long baseline interferometry array data is provided.

[0194] This manual also provides Figure 6 The one shown corresponds to Figure 1 A schematic diagram of the electronic device. Figure 6 As mentioned above, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 1The celestial body imaging method based on the very long baseline interferometer array. Of course, in addition to the software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0195] For the improvement of a technology, it can be clearly distinguished whether it is a hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or a software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with a hardware entity module. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0196] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (e.g., software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, and the memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0197] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0198] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0199] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0200] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0201] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0202] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0203] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0204] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0205] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0206] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0207] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0208] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0209] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0210] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A method for celestial body imaging based on very long baseline interferometry array data, characterized in that: include: receiving an imaging request for a target celestial body and starting a calibration process according to the imaging request; Obtaining, through the calibration process, visibility data of the target celestial body collected by each baseline in a very long baseline interferometer array as initial visibility data corresponding to each baseline; For each baseline, the initial visibility data corresponding to the baseline is calibrated to obtain calibrated visibility data corresponding to the baseline; Determine the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, and start the imaging process; Through the imaging process, the data to be imaged are processed, and the target celestial body is imaged according to the processed data to be imaged, wherein, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain reference data to be imaged, the data to be imaged associated with the abnormal antenna and the reference data to be imaged are processed respectively, and the target celestial body is imaged according to the processed data to be imaged associated with the abnormal antenna and the processed reference data to be imaged; The imaging process includes deleting the data to be imaged associated with the abnormal antenna from the data to be imaged to obtain the reference data to be imaged, processing the data to be imaged associated with the abnormal antenna and the reference data to be imaged respectively, and imaging the target celestial body according to the processed data to be imaged associated with the abnormal antenna and the processed reference data to be imaged, which specifically includes: Deleting the data to be imaged associated with the abnormal antenna from the data to be imaged to obtain reference data to be imaged; Processing the reference data to be imaged to obtain first-level data to be imaged and fixing the first-level data; Restoring the data to be imaged associated with the abnormal antenna in the first level of data to be imaged, and processing the data to be imaged associated with the abnormal antenna to obtain the second level of data to be imaged; releasing the fixed first-level data to be imaged in the second-level data to be imaged to obtain target data to be imaged; The target celestial body is imaged according to the target data to be imaged.

2. The method according to claim 1, characterized in that For each baseline corresponding to the initial visibility data, calibrating the initial visibility data corresponding to the baseline to obtain calibrated visibility data corresponding to the baseline specifically includes: For the initial visibility data corresponding to each baseline, based on the antenna parameters corresponding to each antenna contained in the baseline and the amplitude information and phase information of the reference celestial body corresponding to the target celestial body, the initial visibility data corresponding to the baseline is calibrated to obtain the calibrated visibility data corresponding to the baseline, wherein, for each antenna, the antenna parameters corresponding to the antenna include the attribute information of the antenna itself and the delay information of the antenna in the process of collecting data.

3. The method according to claim 2, characterized in that Based on the antenna parameters corresponding to each antenna contained in the baseline, the amplitude information and phase information of the reference celestial body corresponding to the target celestial body, the initial visibility data corresponding to the baseline is calibrated to obtain the calibrated visibility data corresponding to the baseline, specifically including: Performing amplitude calibration on the initial visibility data according to an average value of the autocorrelation spectrum corresponding to each antenna, the system temperature, and the antenna gain temperature to obtain first-level calibration data; A point-shaped celestial body meeting preset conditions is determined from other celestial bodies observed at the same time as the target celestial body as the reference celestial body, and a bandpass calibration is performed on the first-level calibration data according to the difference between the function of the amplitude and phase of each spectrum window of the reference celestial body in multiple frequency channels varying with the frequency and the benchmark function to obtain the second-level calibration data; The phase of the second-level calibration data is calibrated according to the calculated delay and delay rate corresponding to each antenna to obtain the calibrated visibility data.

4. The method according to claim 1, characterized in that Determining the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, specifically comprising: For each calibrated visibility data corresponding to the baseline, the calibrated visibility data is divided into a plurality of data segments according to a preset time interval, and an average value of each data segment within the time interval is determined; The calibrated visibility data is updated according to an average value of each data segment within the time interval to obtain updated visibility data; According to the updated visibility data corresponding to each baseline, the data to be imaged for the target celestial body is determined.

5. The method according to claim 1, characterized in that Determining the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, specifically comprising: For each baseline, a weight factor corresponding to the baseline is determined according to the baseline parameters of the baseline; According to the calibrated visibility data corresponding to each baseline and the weight factor corresponding to each baseline, each calibrated visibility data is rasterized to obtain the data to be imaged.

6. The method according to claim 1, characterized in that The imaging process processes the data to be imaged, and images the target celestial body according to the processed data to be imaged, specifically including: Determining an initial pulse function according to the data to be imaged; Performing a two-dimensional Fourier transform on the initial pulse function to obtain reference visibility data; Correcting the data to be imaged according to the reference visibility data, and re-determining the reference visibility data according to the corrected data to be imaged; The correction target is that the maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data is less than a preset brightness peak signal-to-noise ratio multiple, and the data to be imaged is corrected several times according to the re-determined reference visibility data to obtain the processed data to be imaged.

7. The method according to claim 6, characterized in that Taking the maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data as a correction target being less than a preset brightness peak signal-to-noise ratio multiple, performing several correction processes on the data to be imaged according to the re-determined reference visibility data to obtain the processed data to be imaged, specifically including: The maximum value of the residual map between the corrected data to be imaged and the re-determined reference visibility data is less than a preset brightness peak signal-to-noise ratio multiple as a correction target, the corrected data to be imaged is corrected several times according to the re-determined reference visibility data, and the target pulse function is determined according to the data to be imaged obtained after the last correction; Performing a two-dimensional Fourier transform on the target pulse function to obtain target reference visibility data; According to the target reference visibility data, the amplitude normalization factor corresponding to each antenna is determined, with the goal of minimizing the error between the gain parameters corresponding to each antenna. The data to be imaged obtained after the last correction is corrected using the amplitude normalization factor to obtain the processed data to be imaged.

8. The method according to any one of claims 6 to 7, characterized in that Imaging the target celestial body according to the processed data to be imaged specifically includes: Performing a two-dimensional inverse Fourier transform on the data to be imaged to obtain a brightness distribution image, and determining a residual image according to a difference between the brightness distribution image and a preset pulse function; Determining a peak point of the residual image in a specified area of ​​the brightness distribution image, and determining dirty beam data according to position information of the peak point and a preset loop gain value; Determine correction data according to the dirty beam data and the preset pulse function, correct the residual image according to the difference between the data value at the peak point and the correction data, and update the preset pulse function according to the peak value in the updated residual image and the loop gain value; Taking the minimization of the peak value in the updated residual image as the correction target, performing several corrections according to the updated impulse function and the updated residual image, and obtaining the correction value corresponding to each correction; The brightness distribution image is reconstructed according to the correction value corresponding to each correction to obtain a target brightness distribution image for the target celestial body.

9. The method according to claim 1, characterized in that Before deleting the data to be imaged associated with the abnormal antenna from the data to be imaged through the imaging process, the method further includes: According to the calibrated visibility data corresponding to each baseline, determining abnormal antennas and non-abnormal antennas among the antennas included in each baseline; The data to be imaged that is associated with the abnormal antenna is determined from the data to be imaged.

10. The method according to claim 9, characterized in that According to the calibrated visibility data corresponding to each baseline, abnormal antennas and non-abnormal antennas are determined from the antennas included in the baselines, specifically including: For each baseline, according to the calibrated visibility data corresponding to the baseline, determine the amplitude information and phase information corresponding to each antenna included in the baseline as the separation information corresponding to each antenna included in the baseline; Determine the average value and standard deviation of the separation information corresponding to each antenna included in each baseline; For each antenna, a deviation value between the separation information corresponding to the antenna and the average value is determined; if the deviation is greater than a preset multiple of the standard deviation, the antenna is regarded as an abnormal antenna; otherwise, the antenna is regarded as a non-abnormal antenna.

11. The method according to claim 1, characterized in that Input parameters involved in the calibration process and the imaging process are calculated and acquired based on observation information matching the very long baseline interferometer array to adapt to visibility data processing of the baseline interferometer array.

12. A celestial body imaging device for very long baseline interferometry array data, characterized in that: include: A receiving module, used to receive an imaging request for a target celestial body and start a calibration process according to the imaging request; An acquisition module, used for acquiring the visibility data of the target celestial body collected by each baseline in a very long baseline interferometer array through the calibration process as initial visibility data corresponding to each baseline; A calibration module, for calibrating the initial visibility data corresponding to each baseline to obtain calibrated visibility data corresponding to the baseline; A determination module, used to determine the data to be imaged for the target celestial body according to the calibrated visibility data corresponding to each baseline, and start the imaging process; An imaging module, used for processing the data to be imaged through the imaging process, and imaging the target celestial body according to the processed data to be imaged, wherein, through the imaging process, the data to be imaged associated with the abnormal antenna is deleted from the data to be imaged to obtain reference data to be imaged, the data to be imaged associated with the abnormal antenna and the reference data to be imaged are processed respectively, and the target celestial body is imaged according to the processed data to be imaged associated with the abnormal antenna and the processed reference data to be imaged; Among them, the imaging module is specifically used to delete the data to be imaged associated with the abnormal antenna in the data to be imaged to obtain benchmark data to be imaged; process the benchmark data to be imaged to obtain first-level data to be imaged and fix them; restore the data to be imaged associated with the abnormal antenna in the first-level data to be imaged, and process the data to be imaged associated with the abnormal antenna to obtain second-level data to be imaged; release the fixed first-level data to be imaged in the second-level data to be imaged to obtain target data to be imaged; and image the target celestial body according to the target data to be imaged.

13. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of claims 1 to 11 is implemented.

14. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method described in any one of claims 1 to 11 is implemented.

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